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From "Jason Rutherglen (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-3529) Add search to HBase
Date Tue, 12 Apr 2011 13:44:06 GMT

    [ https://issues.apache.org/jira/browse/HBASE-3529?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13018835#comment-13018835
] 

Jason Rutherglen commented on HBASE-3529:
-----------------------------------------

I'm working on profiling and optimizing the HDFS random access, so that the Lucene HDFS queries
are the same as native file system access using NIOFSDirectory.  

I think one extremely direct approach is to set the max block size to something above all
Lucene segments files (at runtime via the DFSClient.create method).  This will guarantee that
there is only one underlying java.io.File per HDFS file, and so random access will avoid navigating
block structures (which require expensive network calls, a binary search, and object creation
overhead).

> Add search to HBase
> -------------------
>
>                 Key: HBASE-3529
>                 URL: https://issues.apache.org/jira/browse/HBASE-3529
>             Project: HBase
>          Issue Type: Improvement
>    Affects Versions: 0.90.0
>            Reporter: Jason Rutherglen
>         Attachments: HBASE-3529.patch, lucene-analyzers-common-4.0-SNAPSHOT.jar, lucene-core-4.0-SNAPSHOT.jar,
lucene-misc-4.0-SNAPSHOT.jar
>
>
> Using the Apache Lucene library we can add freetext search to HBase.  The advantages
of this are:
> * HBase is highly scalable and distributed
> * HBase is realtime
> * Lucene is a fast inverted index and will soon be realtime (see LUCENE-2312)
> * Lucene offers many types of queries not currently available in HBase (eg, AND, OR,
NOT, phrase, etc)
> * It's easier to build scalable realtime systems on top of already architecturally sound,
scalable realtime data system, eg, HBase.
> * Scaling realtime search will be as simple as scaling HBase.
> Phase 1 - Indexing:
> * Integrate Lucene into HBase such that an index mirrors a given region.  This means
cascading add, update, and deletes between a Lucene index and an HBase region (and vice versa).
> * Define meta-data to mark a region as indexed, and use a Solr schema to allow the user
to define the fields and analyzers.
> * Integrate with the HLog to ensure that index recovery can occur properly (eg, on region
server failure)
> * Mirror region splits with indexes (use Lucene's IndexSplitter?)
> * When a region is written to HDFS, also write the corresponding Lucene index to HDFS.
> * A row key will be the ID of a given Lucene document.  The Lucene docstore will explicitly
not be used because the document/row data is stored in HBase.  We will need to solve what
the best data structure for efficiently mapping a docid -> row key is.  It could be a docstore,
field cache, column stride fields, or some other mechanism.
> * Write unit tests for the above
> Phase 2 - Queries:
> * Enable distributed Lucene queries
> * Regions that have Lucene indexes are inherently available and may be searched on, meaning
there's no need for a separate search related system in Zookeeper.
> * Integrate search with HBase's RPC mechanism

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